Again, recent evidence has supported that view. Olfactory bulb output to the olfactory cortex varies by subregion. For example, while output from an individual glomerulus projects widely throughout anterior and posterior piriform cortex, projections to the cortical nuclei of the amygdala (COA) are more patchy, with different glomeruli projecting to different locations (Sosulski et al., 2011). Furthermore, all regions of the olfactory bulb project to the piriform cortex, while the COA is more Crizotinib solubility dmso strongly targeted by the dorsal olfactory bulb

(Miyamichi et al., 2011). The loss of odor specific spatial patterns of input in the piriform cortex, and their at least partial maintenance in the COA may suggest a more labeled line mechanism of processing in the COA as opposed to the distributed, content addressable process in the piriform cortex. This more direct, odor-specific processing in COA may contribute to apparent innate hedonic responses to some odors (Khan et al., 2007 and Kobayakawa et al., 2007). The anterior olfactory nucleus (AON) can be divided into several subregions and has a three-layered structure roughly similar to that of the piriform cortex (Brunjes et al., 2005). The principal cell type is the pyramidal cell, and membrane and synaptic properties

of pyramidal cells within the anterior olfactory nucleus are similar to those within the piriform cortex (McGinley and Westbrook, 2011). The majority of AON receives distributed olfactory bulb input, though the AON pars externa is more topographically organized relative to the bulb (Brunjes et al., 2005 and Miyamichi ABT-737 nmr et al., 2011). Individual neurons in AON respond to diverse odorants and odorant mixtures that activate spatially disparate olfactory bulb glomeruli (Lei et al., 2006), suggesting convergence of odorant feature input onto individual AON neurons. There appears to be no odor-specific spatial patterning of activity (Kay et al., 2011), similar to that seen in piriform cortex. In fact, Haberly has hypothesized that much of the initial odorant feature convergence involved

in the early stages of building odor objects may occur in the AON (Haberly, 2001), allowing piriform cortex to perform more higher order associations between the odor objects and hedonics, CYTH4 context and other odors (see below). The olfactory tubercle receives olfactory input dominated by tufted cells from the ventral olfactory bulb (Scott et al., 1980 and Wesson and Wilson, 2011). This input may also show a patchy distribution like the COA, though this has not been quantified (Sosulski et al., 2011). Despite the direct olfactory bulb input, the olfactory tubercle has been primarily studied as a region involved in reward and addiction given its developmental and anatomical association with the ventral striatum (Heimer, 2003 and Ikemoto, 2007).

These “intelligent” forms of feedback control involving the motor cortex are consistent with current theories of optimal feedback control, which go beyond older servomechanistic accounts of the role of sensory feedback in motor control

(Scott, 2004 and Todorov and Jordan, 2002). We have recently examined the effects of somatosensory feedback on the directional tuning of MI neurons by comparing responses during active and passive movements in the awake monkey. As previous MDV3100 datasheet studies have found (Fetz et al., 1980 and Lemon et al., 1976), we observed two distinct populations of MI neurons: one population that fired in an incongruent fashion for passive and active movements of the arm involving coordinated flexion and extension of the shoulder and elbow joints whereas a second population fired in a congruent manner (Suminski et al., 2009). The first “incongruent” neural population had preferred directions that were 180 degrees apart when measured during active and Dasatinib cell line passive conditions (Figure 4A, green bars). During active movement, this subpopulation exhibited a median information lag time of +100 ms (Figure 4B, dark green

curve), which suggested that this population was “driving” movement during voluntary movement. However, during passive movement, this population showed a median directional information peak lag time of –50 ms, indicating that neural modulation lagged movement (Figure 4B, light green curve). This response latency is consistent with long-loop sensory effects on MI reported by others (Fetz et al., 1980, Lemon et al., 1976 and Pruszynski et al., 2011b). If we assume that this population is providing “driving” signals to contract certain muscles during active movement but also receiving spindle afferent information from the same or synergistic muscles, then it would be expected that this cell subpopulation would ADP ribosylation factor show increased firing when the muscles were being stretched during passive movement. The “congruent” neural population exhibited preferred directions that were similar during active and passive movements (see Figure 4A, purple bars). This population led movement by a

median value of +50 ms during active movement (Figure 4C, left panel, dark purple curve). However, in contrast to the incongruent population, the median information peak lag time was 0 ms during passive movement, indicating neural modulation tracked movement direction with no motor lead or sensory lag (Figure 4C, left panel, light purple curve). How do we explain real-time tracking of movement without a sensory lag? One intriguing albeit speculative hypothesis is that this population may be serving to predict the future sensory consequences of motor commands. Evidence from psychophysical and modeling studies suggests that the nervous system can predict the sensory consequences of motor actions (Desmurget and Grafton, 2000 and Nelson, 1996). This function has been traditionally localized to the parietal cortex or cerebellum (Desmurget et al.

A recent

study by Shi et al. (2010) addressed the relative contributions of CNIHs and TARPs to the trafficking and function of synaptic AMPARs. They first measured the properties of AMPARs coexpressed in HEK cells with both CNIH-2 and γ-8 and found slow kinetics, consistent with binding to CNIH-2, and an increased response to kainate, consistent with binding to γ-8. They obtained similar results when CNIH-2 was coexpressed with a TARP-AMPAR fusion construct. Together, these results support the notion that CNIHs and TARPs modulate AMPARs by Regorafenib interacting with distinct binding sites. However, Shi et al. (2010) found that overexpressing CNIH-2 in neurons had only a minor effect on extrasynaptic AMPARs and no evidence for a significant contribution to synaptic AMPAR function. On the contrary, the properties of synaptic AMPARs were most consistent with their exclusive association with TARPs. In support of their electrophysiological Y-27632 chemical structure data, they found that CNIH-2 was barely detectable at the cell surface and that the majority of CNIH-2 expressed in cultured hippocampal neurons

appeared associated with intracellular organelles (colocalization with the cis-Golgi marker GM130). This begs the question: why do CNIHs associate with surface AMPARs in HEK cells but hardly at all in neurons? One possibility is that essential cell biological processes differ between the two cell types such that neurons exclude CNIH from the plasma membrane. However, this contradicts the finding by Kato et al. (2010a) that CNIH-2 contributes to synaptic AMPAR function in transfected neurons. Discrepancies between these two studies might reflect subtle methodological differences in the overexpression studies. Collectively, the

data on CNIHs put us in a bit of a pickle. Kato et al. (2010a) find evidence for a hippocampal tripartite receptor complex containing AMPARs, CNIHs, and TARPs. On the other hand, Schwenk et al. (2009) argue that AMPARs associate with either TARPs or CNIHs in a mutually exclusive manner. Kato et al. (2010a) provide evidence that CNIHs modulate the kinetic properties Rutecarpine of AMPARs in neurons and HEK cells, whereas Shi et al. (2010) find that CNIHs only have significant effects on AMPARs expressed in HEK cells. How can these findings be reconciled? The most obvious starting point is the discovery of resensitization by Kato et al. (2010a), which occurs at a vastly slower timescale than conventional deactivation, desensitization, and EPSCs. Does CNIH-2 have a direct role in modulating resensitization, or an indirect role, perhaps by recruiting additional proteins to the signaling complex? It is curious that resensitization is observed with only a subset of TARPs. Do CNIHs also form tripartite complexes with AMPARs and the TARPs that do not facilitate resensitization? If so, do CNIHs contribute to AMPAR function in these complexes? Perhaps CNIHs have additional functions that are only apparent at longer timescales.

, 2010). In humans, decreased beta-band power in subthalamic nuclei correlates with faster RT, indicating that beta-band activity can also reflect the motor command to initiate movement (Kühn et al., 2004). Beta-band activity is also observed in EEG, and boosting beta-band EEG activity using TMS in humans slows movements themselves (Pogosyan et al., 2009), which is broadly consistent with our results. Because beta-band activity is a widespread property of Dasatinib purchase skeletal-motor circuits, a concern that naturally arises is that beta-band signals in area LIP are not generated locally and result instead from activity that arises in PRR, for example, and passively spreads, through

volume conduction, to area LIP. Although we cannot rule out the influence of volume conduction on our results, the evidence suggests that area LIP beta-band activity is a property of local neural processing within area LIP and is not simply due to volume conduction from PRR. First, beta-band activity in V3d that occurs within 10 mm of PRR and Ruxolitinib as close as area LIP does not show similar selectivity. Second, we

also show that beta-band activity is coherent with spike timing within area LIP, demonstrating a role in local processing. Recent studies show that LFP activity recorded in V1 is predominantly local and does not spread significantly beyond 250 μm (Katzner et al., 2009 and Xing et al., 2009); however, this remains controversial (Kajikawa and Schroeder, 2011). Another concern is that the correlation between beta-band Dichloromethane dehalogenase power and SRT (beta-SRT correlation) results from behavioral correlations between the RTs. However, we believe that beta-SRT correlations do not result simply from RT correlations for two main reasons. First, we show that beta-band power before the go cue correlates with RT following the go cue. Hence, beta-band power does not result from RT. Second, SRT and RRT are not sufficiently correlated to suggest that beta-SRT correlations

imply beta-RRT correlations: we observe that beta-band power can be correlated with SRT but not RRT, and vice versa. We also show that SRT-RRT correlation is smaller during trials when beta-band power in area LIP does not vary. Thus, our data suggest that RT correlations can result from variations in beta-band power and that beta-band power cannot result from RT correlations. To reveal a neural mechanism of coordination, we have used saccade and reach RTs to link neural activity to behavioral coordination. Our results indicate that coherent spike LFP beta-band activity in PPC reflects spatial representations that guide coordinated movement and support the hypothesis that eye-hand coordination involves coordinated movement preparation that is shared between effectors. Two male rhesus monkeys (Macaca mulatta) participated in the experiments. Each animal was first implanted with an MRI-compatible head cap under general anesthesia.

, 2013). These data raise the possibility that Neto proteins play a more wide-ranging role than initially anticipated. Nevertheless, the interesting question that remains to be answered is whether the association of Neto1/2 with KARs could be regulated by physiological signals, and under what circumstances this occurs. Since KARs are fully operational in the absence of Neto, it is possible that two populations of KARs might exist, those with and without Neto probably fulfilling complementary functional roles. Recently,

the group of Maricq has identified in the worm C. elegans SOL-2, a CUB-domain protein that associates with both the related auxiliary subunit SOL-1 and with the GLR-1 AMPAR ( Wang et al., 2012). Like Neto1, see more SOL-2 contributes to the kinetics of receptor desensitization and is an essential component of AMPAR complexes at worm synapses. These data indicate that several different interacting proteins could form the receptor complex at synapses. One unique feature of KARs is that their channel gating requires external monovalent cations and anions. This ion-dependent channel gating differentiates KARs from other ligand-gated channels, including

the closely related NMDARs and AMPARs (Paternain et al., 2003 and Bowie, 2002; see Bowie, 2010 for a review). Indeed, crystallographic studies have revealed the existence of an ion binding pocket in KAR subunits (Plested and Mayer, 2007 and Plested et al., 2008). The absolute requirement of ion binding for channel opening this website indicates that KAR activity would be abolished if this binding site remained unoccupied, prompting the suggestion that this site might be used as a target for specific allosteric modulation

of KARs by external agents. The question as to what might be the physiological role of such a strict dependence of the channel gate has not been answered yet but prompted some possibilities. PDK4 For instance, under intense neuronal activity, a situation under which external Na+ levels drop, activation of KARs would be limited, constituting a brake for tissue damage. Indeed, a large fraction of KARs seems to have unoccupied the cation binding site at physiological salt concentrations, making them insensitive to activation by released glutamate (Plested et al., 2008). Much work remains to be done to figure out whether this fraction of incompetent KARs could be modulated up and down as a way to regulate the weight of these receptors in, for instance, synaptic transmission. High-resolution structural analysis has revealed many similarities between the three glutamate receptor families. However, unlike AMPA and NMDA receptors, KARs appear to also signal through an unconventional metabotropic mechanism involving G proteins and second messengers at inhibitory CA1 hippocampal synapses (Rodríguez-Moreno et al.

GABA acts on rod DBCs through two types of GABA receptors, GABAAR and GABACR, which are both chloride channels (e.g., Chávez et al., 2010, Lukasiewicz and Shields, 1998 and McCall et al., 2002). Therefore, we examined b-wave responses in mice in which individual GABA receptor expression was eliminated or pharmacologically blocked. We first analyzed GABACR knockout (GABACR−/−) mice ( McCall et al., 2002) and found that they display a phenotype strikingly similar to that of D1R−/− mice ( Figures

2A and 2B). GABACR−/− and D1R−/− mice had both a substantial reduction in dark sensitivity (∼40% and ∼55%, respectively) and a compression of the operational range. In contrast, blocking GABAAR-mediated input pharmacologically (there is no knockout available that removes all GABAARs RNA Synthesis inhibitor from DBCs) did not affect either dark sensitivity or operational range of rod-driven b-waves ( Figure 2B). These data reveal that GABACRs regulate the light sensitivity of rod-driven DBCs and raise the possibility that the effect of the D1R knockout may be explained by an altered GABACR-mediated input onto rod-driven DBCs. We also note that these GABACR-mediated

effects on rod DBCs cannot be explained by altered rod photoreceptor synaptic output because rods do not express GABACRs ( Enz et al., 1995). In reciprocal experiments, we measured ERG responses after intraocular injections of GABA (Figures 2C and 2D). As has been reported previously, GABA increased b-wave amplitudes (Naarendorp to and Sieving, 1991 and Robson et al., 2004). Despite this amplitude increase, GABA did not affect Ku-0059436 mw the sensitivity or operational range of b-wave responses in WT mice (Figure 2D). Although GABA injections into D1R−/− mice also increased b-wave amplitudes, in this case both the light sensitivity and operational range of b-waves were restored

to those observed in WT animals (these phenomena were phenocopied in WT mice after pharmacological block of D1R; Figure S1). These data show that the lack of D1R-mediated signaling can be completely masked by exogenous GABA, consistent with a role for D1R in modulating a GABAergic input onto rod-driven DBCs. Interestingly, intraocular injections of glycine, which normally provides lateral feedback onto rod DBC axons via chloride currents through glycine receptor channels, reduced the b-wave sensitivity functions in both WT and D1R−/− mice, rather than restoring the loss of sensitivity and operational range in the D1R knockout, as in the case of GABA injections into the eyes of D1R−/− mice ( Figure S3). This suggests that the GABAC-dependent mechanism revealed in this study is specific and implies a difference in ionic microenvironments surrounding GABACR and glycine receptors. Our hypothesis that D1R mediates a GABAergic input onto rod-driven DBCs predicts that pharmacological blockade of GABACRs should not further reduce b-wave sensitivity in D1R−/− mice.

Similarly, moderate changes in neuronal firing measured in visual cortex after visual deprivation can invoke homeostatic selleck kinase inhibitor plasticity, leading

to the restoration of baseline firing rates (Keck et al., 2013 and Hengen et al., 2013; see also Deeg and Aizenman, 2011). Early efforts to model homeostatic plasticity in the stomatogastric system have emphasized that multiple activity sensors are necessary to discriminate quantitative differences in neuronal firing (Liu et al., 1998). Yet, biologically, a system of coordinated sensors with the fidelity to follow neural activity remains unknown. An interesting possibility is that metabolic sensors may be employed in addition to, or in parallel with, changes in intracellular calcium. In dissociated hippocampal culture, eukaryotic elongation factor 2 (eEF2) has been implicated as a sensor that can detect disruption of glutamatergic transmission (Sutton et al., 2004 and Sutton et al., 2007). Additional work implicates a function for TOR-dependent signaling downstream of AMPA receptor blockade (Henry et al., 2012). The potential importance of this signaling system for homeostasis in vivo is emphasized in experiments demonstrating that TOR signaling is essential for balanced network excitation and inhibition (Bateup et al., 2013). The importance of TOR is also emphasized by work at the Drosophila NMJ in vivo, showing that genetic disruption

of TOR and S6 Kinase signaling blocks the sustained expression of presynaptic homeostasis ( Penney et al., Vorinostat in vivo 2012). In many systems, TOR signaling is used to detect qualitative changes in the cellular environment and, thereby, regulates cellular homeostasis and growth ( Laplante and Sabatini, 2012). As such, it is a candidate for detecting quantitative changes

Astemizole in neural function and stimulating downstream homeostatic plasticity. Synaptic scaling is expressed as a change in neurotransmitter receptor abundance. Although a great deal has been discovered about the transcription, assembly, and trafficking of glutamate receptors, the mechanisms that control receptor trafficking in a homeostatic fashion remain largely unknown. Many key issues remain to be molecularly defined, including how synaptic scaling is achieved in a cell-wide fashion with proportional effects at every active zone. Similarly, there is very little information to explain how the synaptic scaling mechanism becomes limited as neuronal firing properties are restored toward baseline, set point levels, and how the system is eventually shut off (but see Tatavarty et al., 2013). In attempting to define how the homeostatic control of glutamate receptor trafficking is achieved, it is useful to make comparisons to nonneuronal systems in which homeostatic control of surface transporters and ion channels has been defined without the added complexity of cell diversity.

Worldwide, irrespective of mechanisms of healthcare funding, there is a desire for delivery of quality patient care at reduced cost. Although different healthcare systems and patient populations will generate differential cost savings, a general move towards day case thyroidectomy would have financial gains. Overall costs of day case compared to inpatient surgery are smaller but possibly less so for thyroid surgery, particularly if efficiencies in the delivery of postoperative care on short stay units are optimised. The cost saving of 30% in one study [18] related to charges rather than true costs, the latter being amenable to savings from appropriate staffing.

Even with costs predominantly relating to operation and recovery SCH 900776 order room time in the US savings of around $2500 per ambulatory case are reported [15] and [16]. In the United Kingdom, the saving of one night stay equates to around £400, around a fifth of the National Health Service’s remuneration GW-572016 supplier for this procedure. In the US, cervical blocks combined with monitored anaesthesia care in preference to general anaesthesia has shown a reduction in postoperative operative narcotics, time in operating room and length of stay [15]. Day case thyroid surgery is feasible but the unpredictable nature of postoperative haematoma and its potential

for life threatening airway compromise tips the balance against the benefits. For some, its’ use for low risk cases is justifiable provided it is undertaken in conjunction with robust postoperative care pathways and retention of those patients where there is concern [6] and [24] but for others [5] and [9], the 23-hour model is the preferred compromise. Quality improvement by continuous outcome monitoring may help define those most at risk of bleeding and further minimise it by more widespread specialisation with improved because outcomes from high volume surgeons [31]. the authors declare that they have no conflicts of interest concerning this article. “
“Saraca asoca [Roxb.], De. Wild [Indian name; Ashoka] belongs to family Caesalpinaceae. The earliest chronicles mention this tree in the Indian ayurvedic treatise and Charaka Samhita [100 A.D.],

where the plant has been recommended to treat various gynecological disorders. In another treatise i.e. Bhavprakasha Nighantu, this plant has been referred as a uterine tonic for regularizing the menstrual disorders. Its bark has a stimulating effect on endometrium and ovarian tissues and is useful in menorrhagia during uterine fibroids. Flowers of S. asoca are used to treat cervical adenitis, biliousness, syphilis, hyperpiesia, burning sensation, hemorrhagic dysentery, piles, scabies in children and inflammation. Plant is also reported to have spasmogenic, anti-ulcer, 1 anti-oxytocic, anti-depressents, 2 anti-inflammatory, 3 anti-oxidative, anti-bacterial, 4 anti-larval, anti-implantation, anti-tumor, anti-progestational, anti-estrogenic and anti-cancer 5 activities.

Modeling suggests that even a small dendritic voltage gradient in combination with voltage-gated channels could generate a robust DS signal in SAC dendrites (Hausselt et al., 2007). In another model it was proposed, that SACs generate a Cl− concentration gradient along their dendrites due to a differential distribution of Cl− intruders and extruders, and that this results in GABAergic input causing depolarization at the proximal and hyperpolarization selleckchem at the distal dendrite, respectively (for details see Enciso et al., 2010, Gavrikov et al., 2003 and Gavrikov et al., 2006). According to this model, the asymmetry in the effect

of GABAergic inputs leads to dendritic direction selectivity. Other than the voltage gradient model, the Cl− gradient model requires GABAergic input and therefore does not account for the finding that SAC responses remain DS in the presence of GABA receptor blockers (see below). Ultrastructural (Millar and Morgan, 1987) and

functional data (Zheng et al., Dasatinib in vivo 2004) indicate that mature SACs form reciprocal GABAergic synapses, which have been implicated in the computation of DS signals (e.g., Münch and Werblin, 2006). If a SAC is excited, it inhibits its neighbor—this in turn reduces the neighbor’s GABA release and in effect enhances the first SAC’s response. Such interaction may sharpen the DS contrast in neighboring SAC dendrites pointing in opposite directions (Lee et al., 2010 and Lee and Zhou, 2006). However, since GABA receptor antagonists do not abolish dendritic direction selectivity in SACs (Euler et al.,

2002, Hausselt et al., 2007 and Oesch and Taylor, 2010), it is unlikely that these interactions are essential for the SAC’s intrinsic DS mechanism. In addition to inhibition, DS ganglion cells receive DS excitatory input from bipolar cells (Fried et al., 2005). This tuning could arise from DS suppression of bipolar cell output by GABAergic amacrine cells (Figure 5E), which would explain why this excitatory DS pathway is eliminated by ADP ribosylation factor GABA receptor blockers (see The Role of Inhibition). Because ablating SACs abolishes ganglion cell DS responses (Amthor et al., 2002 and Yoshida et al., 2001), it is likely that SACs are involved in tuning bipolar cell output—if other amacrine cells were crucial, some residual direction selectivity after ablation would be expected. Besides glutamatergic excitation, DS ganglion cells also receive excitatory cholinergic input from SACs (reviewed in Vaney et al., 2001). Blocking cholinergic receptors in the presence of GABA receptor antagonists reduces the responses of DS ganglion cells independent of motion direction (Chiao and Masland, 2002), suggesting that cholinergic excitation provides motion-sensitive but not DS excitation (He and Masland, 1997). On the other hand, there is also evidence that this cholinergic input is DS (Figure 5C, Fried et al., 2005 and Lee et al., 2010).

Arrays were analysed on a PCS4000 ProteinChip Reader using the Protein Chip software version 3.0.6 (Ciphergen Biosystems, Inc., selleck kinase inhibitor Fremont, CA). The protocol averaged 10 laser shots per pixel with a focus mass of 24,000 Da, a matrix attenuation of 1000 Da and a range of 0–200,000 Da. The All-in-1 Protein Standard II (BioRad) was analysed on an NP20 array using the same analysis protocol. The following peaks were identified in the resulting spectrum and used to create

an internal calibration: hirudin BHVK (6964.0 Da), bovine cytochrome c (12230.92 Da), equine cardiac myoglobin (16951.51 Da) and bovine carbonic anhydrase (29023.66 Da). This internal calibration was applied to the spectra as an external calibration. The presented data are baseline subtracted and normalized by total ion current. Peaks with a signal-to-noise (S/N) ratio below 7 were not considered in subsequent analysis. FMDV antigen concentrated by PEG6000 precipitation is normally used for selleck chemical vaccine preparation. Such crude antigen preparations contain many proteins, most of

which are presumably derived from the BHK-21 cells used for virus propagation, as can be revealed by SDS-PAGE analysis (Fig. 1) of strains O1 Manisa (lane 2), Asia 1 Shamir (lane 4) and A24 Cruzeiro (lane 6). When the FMDV antigen of these strains is further purified by ultracentrifugation through a sucrose cushion it predominantly consists of three proteins migrating at about 23–25 kDa (Fig. 1, lanes 3, 5 and 7) which presumably represent VP1, VP2 and VP3. To facilitate the identification of the spectral peaks corresponding to the FMDV structural proteins

we used these purified antigens in SELDI-TOF-MS analysis employing NP20 arrays, which binds all proteins (Fig. 2a–c). The spectral peaks found were compared to the molecular masses predicted by translation of the RNA sequences (Table 1). For all three strains the peak at 9.0 kDa corresponds to myristoylated VP4, the peak at 23.2–23.3 kDa corresponds to VP1 and the peak at 24.5–24.9 kDa corresponds to VP2. Since these peaks are quite broad an accurate determination of their molecular mass is difficult. The molecular mass of VP3 is predicted to be intermediate between VP1 and VP2 (Table 1). A peak corresponding either to VP3 is more difficult to identify. Only in the profile of strain O1 Manisa a small peak can be seen at 24.1 kDa that could represent VP3 (Fig. 2c). The peak at 48 kDa that is observed with strain O1 Manisa but not with the two strains of other serotypes corresponds quite well to a VP1–VP2 dimer (Fig. 2c). For each serotype we also observe peaks of lower height at a normalized mass (m/z) of about 11.6 and 12.2 kDa, which is half the molecular mass of VP1 and VP2, and therefore represents double protonated forms of these proteins. For all three strains a repetitive pattern of peaks that differ by about 24 kDa is present in the molecular range above 50 kDa.